Abstract
The automatic subcellular localisation of proteins in living cells is a critical step in determining their function. The evaluation of fluorescence images constitutes a common method of localising these proteins. For this, additional knowledge about the position of the considered cells within an image is required. In an automated system, it is advantageous to recognise these cells in bright-field microscope images taken in parallel with the regarded fluorescence micrographs. Unfortunately, currently available cell recognition methods are only of limited use within the context of protein localisation, since they frequently require microscopy techniques that enable images of higher contrast (e.g. phase contrast microscopy or additional dyes) or can only be employed with too low magnifications. Therefore, this article introduces a novel approach to the robust automatic recognition of unstained living cells in bright-field microscope images. Here, the focus is on the automatic segmentation of cells.
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Huh, W.K., Falvo, J.V., Gerke, L.C., Carroll, A.S., Howson, R.W., Weissman, J.S., O’Shea, E.K.: Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003)
Liebel, U., Starkuviene, V., Erfle, H., Simpson, J.C., Poustka, A., Wiemann, S., Pepperkok, R.: A microscope-based screening platform for large-scale functional protein analysis in intact cells. FEBS Letters 554, 394–398 (2003)
Murphy, R.F., Velliste, M., Porreca, G.: Robust numerical features for description and classification of subcellular location patterns in fluorescence microscope images. Journal of VLSI Signal Processing 35, 311–321 (2003)
Chen, X., Murphy, R.F.: Interpretation of Protein Subcellular Location Patterns in 3D Images Across Cell Types and Resolutions. In: Hochreiter, S., Wagner, R. (eds.) BIRD 2007. LNCS (LNBI), vol. 4414, pp. 328–342. Springer, Heidelberg (2007)
Tscherepanow, M., Kummert, F.: Subcellular localisation of proteins in living cells using a genetic algorithm and an incremental neural network. In: Bildverarbeitung für die Medizin 2007, pp. 11–15. Springer, Heidelberg (2007)
Tsien, R.Y.: The green fluorescent protein. Annual Review of Biochemistry 67, 509–544 (1998)
Tscherepanow, M., Zöllner, F., Kummert, F.: Aktive Konturen für die robuste Lokalisation von Zellen. In: Bildverarbeitung für die Medizin 2005, pp. 375–379. Springer, Heidelberg (2005)
Debeir, O., Ham, P.V., Kiss, R., Decaestecker, C.: Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. IEEE Transactions on Medical Imaging 24, 697–711 (2005)
Ray, N., Acton, S.T., Ley, K.: Tracking leukocytes in vivo with shape and size constrained active contours. IEEE Transactions on Medical Imaging 21, 1222–1235 (2002)
Zimmer, C., Labruyère, E., Meas-Yedid, V., Guillén, N., Olivo-Marin, J.C.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: A tool for cell-based drug testing. IEEE Transactions on Medical Imaging 21, 1212–1221 (2002)
Nattkemper, T.W., Wersing, H., Ritter, H., Schubert, W.: A neural network architecture for automatic segmentation of fluorescence micrographs. Neurocomputing 48, 357–367 (2002)
Raman, S., Maxwell, C.A., Barcellos-Hoff, M.H., Parvin, B.: Geometric approach to segmentation and protein localization in cell culture assays. Journal of Microscopy 225, 22–30 (2007)
Schubert, W., Friedenberger, M., Bode, M., Philipsen, L., Ritter, H., Nattkemper, T.W.: Automatic recognition of muscle invasive T-lymphocytes expressing dipeptidyl-peptidase IV (CD26), and analysis of the associated cell surface phenotypes. Journal of Theoretical Medicine 4, 67–74 (2002)
Long, X., Cleveland, W.L., Yao, Y.L.: Effective automatic recognition of cultured cells in bright field images using Fisher’s linear discriminant preprocessing. Image and Vision Computing 23, 1203–1213 (2005)
Long, X., Cleveland, W.L., Yao, Y.L.: Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure. Computers in Biology and Medicine 6, 339–362 (2006)
Tscherepanow, M., Zöllner, F., Kummert, F.: Classification of segmented regions in brightfield microscope images. In: Proceedings of the International Conference on Pattern Recognition (ICPR), vol. 3, pp. 972–975. IEEE, Los Alamitos (2006)
Wu, K., Gauthier, D., Levine, M.: Live cell image segmentation. IEEE Transactions on Biomedical Engineering 42, 1–12 (1995)
Chen, X., Yu, C.: Application of some valid methods in cell segmentation. In: Proceedings of SPIE, vol. 4550, pp. 340–344 (2001)
Grobe, M., Volk, H., Münzenmayer, C., Wittenberg, T.: Segmentierung von überlappenden Zellen in Fluoreszenz- und Durchlichtaufnahmen. In: Bildverarbeitung für die Medizin 2003, pp. 201–205. Springer, Heidelberg (2003)
Malpica, N., de SolĂłrzano, C.O., Vaquero, J.J., Santos, A., Vallcorba, I.M., GarcĂa-Sagredo, J., del Pozo, F.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 23, 289–297 (1997)
Walker, R.F., Jackway, P.T., Lovell, B.: Classification of cervical cell nuclei using morphological segmentation and textural feature extraction. In: Australian and New Zealand Conference on Intelligent Information Systems, pp. 297–301 (1994)
Perner, P., Jänichen, S., Perner, H.: Case-based object recognition for airborne fungi recognition. Artificial Intelligence in Medicine 36, 137–157 (2006)
Alexopoulos, L.G., Erickson, G.R., Guilak, F.: A method for quantifying cell size from differential interference contrast images: validation and application to osmotically stressed chondrocytes. Journal of Microscopy 205, 125–135 (2002)
Young, D., Gray, A.J.: Cell identification in differential interference contrast microscope images using edge detection. In: Proceedings of the 7th British Machine Vision Conference (BMVC), vol. 1, pp. 133–142. BMVA Press (1996)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, New York (2003)
Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19, 41–47 (1986)
Soille, P., Breen, E.J., Jones, R.: Recursive implementation of erosions and dilations along discrete lines at arbitrary angles. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 562–667 (1996)
van Herk, M.: A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels. Pattern Recognition Letters 13, 517–521 (1992)
Williams, D.J., Shah, M.: A fast algorithm for active contours and curvature estimation. Computer Vision, Graphics, and Image Processing: Image Understanding 55, 14–26 (1992)
Cohen, L.D.: Note: On active contour models and balloons. Computer Vision, Graphics, and Image Processing: Image Understanding 53, 211–218 (1991)
Fitzgibbon, A.W., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 476–480 (1999)
Tscherepanow, M., Jensen, N., Kummert, F.: Recognition of unstained live Drosophila cells in microscope images. In: Proceedings of the International Machine Vision and Image Processing Conference (IMVIP), pp. 169–176. IEEE, Los Alamitos (2007)
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Tscherepanow, M., Zöllner, F., Hillebrand, M., Kummert, F. (2008). Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images. In: Perner, P., Salvetti, O. (eds) Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry. MDA 2008. Lecture Notes in Computer Science(), vol 5108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70715-8_13
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DOI: https://doi.org/10.1007/978-3-540-70715-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70714-1
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